The Wealth Dynamics of Entrepreneurship for Black and White ...

THE WEALTH DYNAMICS OF ENTREPRENEURSHIP FOR BLACK AND WHITE FAMILIES IN THE U.S.

William D. Bradford University of Washington

Current Version: September 2002

JEL Classifications: J10, J15, O15

Address: School of Business Administration Box 353200 University of Washington Seattle, WA 98195-3200 e-mail: [email protected] Previous versions of this paper were presented at the 2000 session of the National Economic Association, and the 2002 session of the Midwest Economic Association. I am appreciative of helpful comments from those at these sessions and from Alan Hess, Paul Malatesta and Wayne Ferson. I am also grateful for helpful suggestions of the referees.

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THE WEALTH DYNAMICS OF ENTREPRENEURSHIP

FOR BLACK AND WHITE FAMILIES IN THE U.S.

ABSTRACT

Among black and white families, entrepreneurs hold disproportionately more wealth than

workers. Black entrepreneurs hold a lower fraction of black family wealth than white

entrepreneurs hold of white family wealth, because black families have a lower rate of

entrepreneurship. Black and white entrepreneurs have more upward and less downward mobility

in the wealth distribution than black and white workers, respectively. The black entrepreneurs

and white entrepreneurs have similar upward mobility and black entrepreneurs less downward

mobility in the wealth distribution. The entrepreneurs save at higher rates than workers, and the

saving rates of black entrepreneurs and white entrepreneurs are not found to differ.

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THE WEALTH DYNAMICS OF ENTREPRENEURSHIP FOR BLACK AND WHITE FAMILIES IN THE U.S.

I. Introduction

Consider family wealth (or net worth), the sum of the family’s assets less its debts. This

study links data on family wealth accumulation and the decision of individuals to start or run their

own firms—i.e., engage in entrepreneurship. Only recently have scholars began to intensely

compare the wealth accumulation of entrepreneurs relative to workers. Quadrini (1998, 1999)

and Gentry and Hubbard (2000) show that entrepreneurs are more upwardly mobile in the wealth

distribution, and achieve higher wealth levels and wealth-income ratios than workers. Gentry and

Hubbard also show that the saving rate of entrepreneurs is higher than that of workers.

This paper augments those studies along three lines. First, I separate entrepreneurs by

race and compare black entrepreneurs with white entrepreneurs. There is a growing literature on

the participation of ethnic minorities in entrepreneurship, particularly the relatively low

entrepreneurship rate of blacks in the U.S.1 Numerous writers have promoted the engagement of

blacks in entrepreneurship as a way to significantly reduce the wealth disparity between black and

white Americans.2 Heretofore, no study has documented to what extent both black and white

entrepreneurs actually achieve higher wealth-levels when one controls for education, age and

other factors that ordinarily affect wealth accumulation.3 Second, I control for demographic and

other relevant factors when comparing the wealth transitions of entrepreneurs with workers. The

1 For example, see Bates (1997), Fairlie (1999), Fairlie and Meyer (2000) and Hout and Rosen (2000). 2For example, see Boston (1999), Wallace (1993) and Butler (1991). This idea goes at least as far back as the early 1900s. See Harmon, et. al (1929). 3 Various studies have compared the earnings of the entrepreneurs with wage/salary workers. See Aronson (1991), Devine (1994), Ferber and Waldfogel (1998), and Hamilton (2000). Some studies have found that wage/salary earnings exceed those of the entrepreneurs in the U.S., while others have found the reverse. One problem with these studies is that the entrepreneurs tend to underreport earnings in order to reduce tax liabilities. I know of no research on how underreporting varies according to the differing characteristics of the entrepreneurs. Another problem is the proper adjustment to earnings for certain benefits (e.g., health) that more wage/salary workers receive than do the entrepreneurs. Wealth accumulation is an obvious alternative to earnings. To the extent that underreported income and the net impact of benefits show up in personal assets, then wealth is better than earnings as a measure of the economic impact of entrepreneurship.

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transition matrices of Quadrini and Gentry and Hubbard do not control for factors that would

ordinarily affect the relative wealth change. Third, I use a larger sample and more descriptive

personal variables than Gentry and Hubbard in comparing the saving rates of entrepreneurs with

those of workers.

I frame the analysis into three questions. First, to what extent do both black and white

entrepreneurs hold higher levels of wealth than workers, when controlling for relevant variables?

Second, are both black and white entrepreneurs upwardly mobile in the wealth distribution,

before and after adjusting for personal characteristics? Third, do both black and white

entrepreneurs display higher wealth-income ratios and saving rates than workers? My results in

response to these questions are as follows.

First, both black and white entrepreneurs hold higher fractions of wealth relative to their

fraction of the population in their racial groups. But black entrepreneurs hold a lower fraction of

black family wealth than white entrepreneurs hold of white family wealth. The reason is the

lower rate of entrepreneurship among black families rather than a lower relative wealth advantage

of black entrepreneurs over black workers. These are univariate analyses. Ordinary least squares

(OLS) and Quantile (median) regressions show that controlling for education, age and other

relevant variables, both white and black entrepreneurs hold more wealth than the other categories

(including workers) within their race groups, and the absolute wealth advantage of white

entrepreneurs over white workers is larger than that of black entrepreneurs over black workers.

While the wealth advantage of white workers over black workers is statistically significant in the

OLS regression and not statistically significant in the median regression, the wealth advantage of

white over black entrepreneurs is statistically significant at the 0.10 level for both the OLS and

median regressions. The data come from the Panel Study of Income Dynamics (PSID), and do not

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capture the very top of the wealth distribution, which is disproportionately white. The results

here should be considered as referring to wealth up to the 98th percentile.4

The second question refers to wealth mobility. Here I trace the changes in wealth for

entrepreneurs and workers over 1984-89 and 1989-94. The resulting transition matrices show

that both black and white continuing entrepreneurs have more upward mobility and less

downward mobility in the wealth distribution than continuing workers in their respective race

group. The transition matrices also show that white entrepreneurs have more upward mobility

and less downward mobility than black entrepreneurs. But these matrices do not control for

variables other than work category. Logistic regressions that control for variables such as

education, age and receipt of a gift or inheritance, show that race is not statistically significant in

predicting the upward mobility of entrepreneurs, and that black continuing entrepreneurs display

less downward mobility than white continuing entrepreneurs.

The third question relates to the saving rates of the black and white entrepreneurs. Here

the univariate comparisons show that the wealth-income ratios of both black and white

entrepreneurs are higher than those of workers in their racial groups, and the wealth-income ratios

of white entrepreneurs are higher than those of black entrepreneurs. The OLS and median

regressions confirm that white entrepreneurs have higher wealth-income ratios than white

workers and black entrepreneurs. But the regressions also indicate that the wealth-income ratios

of the black entrepreneurs are equal to (OLS regression) or lower than (median regression) those

of black workers. The results for saving rates differ somewhat. The saving rates of black and

white entrepreneurs are higher than those of black and white workers, respectively, and the black

and white entrepreneurs’ saving rates are not found to differ.

Several observations flow from these results. First, Quadrini (1999) concluded that

entrepreneurship tends to increase the concentration of wealth in the U.S. The results of this study

4 Wealth data from the PSID line up reasonably closely through the 98th percentile with data from the Survey of Consumer Finances, which oversamples high wealth families. See Juster et al. (1999).

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indicate that entrepreneurship among black families has reduced wealth concentration by shifting

more wealth to black families, who are disproportionately at the lower end of the wealth

spectrum. Second, one must be careful in projecting the effect of more black entrepreneurs on

wealth concentration. This study documents the favorable impact on wealth of black

entrepreneurs compared to black workers. But what are the influences that produce the favorable

results of black entrepreneurs, and can these influences be transferred to new black business

entrants? Further research is needed before we can project that higher rates of black

entrepreneurship will further reduce the wealth gap between black and white Americans.

Third, the saving rates of black entrepreneurs are higher than those of black workers, and

are not found to differ from those of white entrepreneurs (controlling for demographic variables).

Part of the difference between black families and white families in the saving rate might come

from the lower rate of entrepreneurship among black families. Thus it is useful to understand the

root causes of these higher saving rates and the extent to which new black entrepreneurs will

display these higher saving rates. More broadly, the findings here suggest that research on family

saving decisions in general and the saving decisions of wealthy and high-income families should

encompass the role of entrepreneurship in affecting such decisions.

The rest of this paper is organized as follows. Part II analyzes the predictive content of

race and entrepreneurship in determining the wealth accumulation of families. Part III examines

the extent to which race and participation in entrepreneurship affect the change in the family’s

position in the wealth distribution over 1984-89 and 1989-94. Part IV analyzes the wealth-

income ratios and saving rates of families, as associated with race and participation in

entrepreneurship. Part V overviews and discusses the findings of the study.

II. Are Black and White Entrepreneurs Wealthier?

A. Entrepreneurship and Wealth

What should be expected about the relative wealth of entrepreneurs compared to that of

workers? Several theoretical models of entrepreneurship exist in the economics literature. Lucas

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(1978) assumes that there is a distribution of managerial talent across individuals in the work

force. Those who become entrepreneurs are those with the most managerial talent. One can

extend this model to consider any talent that can result in higher income under entrepreneurship,

such as financial acumen (the latter is suggested by a referee). In Kihlstrom and Laffont ‘s (1979)

model, the decision to become an entrepreneur is based on a comparison of the risky return to

self-employment to the less risky return of wage/salary work.5 In Evans and Jovanovic (1989),

the individual chooses the work sector that provides the highest expected net income, but the

choice is subject to a liquidity constraint. Jovanovic (1982) derives a dynamic model in which

firms discovering that they are efficient survive and expand output, while firms discovering that

they are not efficient fail. Proceeding from each of the models is the conclusion that earnings

over time should be greater for entrepreneurs than for wage/salary workers. The higher earnings

then lead to higher wealth creation. In contrast, Hamilton (2000) expresses the argument that

entrepreneurs may trade lower earnings for the nonpecuniary benefits of business ownership.

Entrepreneurship offers greater freedom and control in the work place, and workers may choose

self-employment despite self-employment earnings below their paid employment alternative.6

Here the earnings and wealth of entrepreneurs may not be higher than those of workers.

The theoretical frameworks of Gentry and Hubbard, and Quadrini (1999) directly focus

on wealth. They conclude that entrepreneurs should hold higher wealth than workers because of

three factors. The first factor is the incentive of a household to accumulate the minimal capital

requirements needed to engage in entrepreneurship or to implement larger projects. The second

factor relates to the uninsurable entrepreneurial risk encountered by business households.

Because entrepreneurs face greater financial risk than workers and are risk averse, their patterns

of savings are more conservative. The third factor that underlies the difference or change in

saving behavior results from the cost of external financing available to potential entrepreneurs.

5Carroll (1994) and Fairlie and Meyer (1997) provide empirical evidence that the return to self-employment is more risky than that of the wage/salary status.

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The high interest rate paid on borrowing increases the marginal return on saving for those

entrepreneurs whose level of wealth is lower than the level of capital invested in their business.

In conclusion, the tradeoff of employment earnings for the nonpecuniary benefits of

entrepreneurship can lead to the hypothesis of lower wealth for entrepreneurs; but the various

other approaches imply that entrepreneurs will have higher wealth than workers.

B. Differences in Wealth Accumulation

Table 1 shows the means and medians of family wealth in 5-year intervals 1984-1999,

inclusive, reported in 1999 dollars.7 [Place Table 1 here] The wealth statistics are weighted

cross-sectional snapshots of the families’ financial traits. Table 1 includes wealth statistics on all

four standard work categories: entrepreneurs (self-employed),8 workers, retired, and

unemployed. The mean and median family wealth of both the black and white entrepreneurs are

larger than any of the other work categories in their racial groups. Indeed, the mean and median

wealth of black and white entrepreneurs are at least twice the overall mean and median of their

racial groups. But the black entrepreneurs differ from the white entrepreneurs in at least three

respects.

First, the mean and median wealth of the black entrepreneurs are lower than those of the

white entrepreneurs: the black-white ratio for the mean and median wealth of entrepreneurs are

0.21 and 0.33, respectively in 1994. But both of these compare favorably with the black-white

group mean and median ratios of 0.20 and 0.12, respectively. Second, entrepreneurs are a lower

fraction of the black families than of white families. In 1994, for example, the black

6 Hamilton cites studies that are consistent with this view. 7 All dollar figures are in 1999 dollars throughout the paper. The CPI-U-X1 is used for calculating rea1 l999 monetary values. 8Two possible definitions of entrepreneurs emanate from the PSID data. One question is “Did you (Head) or anyone else in the family own a business at any time during the previous year or have a financial interest in any business enterprise?” and the second is “In your main job, are you (Head) self-employed or do you work for someone else? Those answering “yes” to the first definition can be workers who have a minority interest in a small business. A “yes” to self-employed in the second definition means that at least the person’s human capital is at risk in the venture. There is substantial overlap in respondents that answer yes to both questions, but the two sets are not identical. I use the self-employment definition. The results of the tests are similar using both methods.

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entrepreneurs were 3.7% of black families compared to 12.8% for white families. This reflects

the lower rate of business ownership observed and analyzed in previous studies.9 Third, the

black entrepreneurs hold a lower percentage of total black family wealth than the white

entrepreneurs of total white family wealth. While black entrepreneurs hold 13.7% of black

family wealth, white entrepreneurs hold 31.1% of white family wealth. The lower ratio of wealth

for black entrepreneurs results primarily from the lower percentage of black entrepreneurs among

black families. Both the mean and median entrepreneurs to group wealth ratios for black

entrepreneurs (2.70 and 8.00) are higher than for white entrepreneurs (2.52 and 2.90).

C. OLS and Median Regressions Predicting Wealth

Table 1 shows that entrepreneurs hold higher levels of wealth than other work categories.

But such univariate comparisons do not control for demographics and other factors that might

cause differences in wealth. For example, life cycle analyses conclude that younger adults hold

less wealth than older adults. Thus multivariate models will be used to examine the relationship

between wealth and entrepreneurship while controlling for the impact of other variables. In

order to test the statistical significance of the differences in wealth between entrepreneurs,

workers and other categories, I estimate a model in which the family’s wealth is regressed on

predictor variables, including work category and race. Let Xi be a vector of independent

variables for family i. The basic model specifies the level of wealth to be linear in Xi:

Wi = a + Xiβi + εi

where Wi , a, βi , and εi are wealth for family i, the regression intercept, the slope parameters, and

the error term, respectively. The variables in the regressions are described as follows:

Black: African American dummy, black head of household = 1; white = 0;

Age: Young = less than 35, Middle = 35 - 54, Old = 55 or older.

Male: Male = 1, Female = 0.

9 See Bates (1997), Fairlie (1999), Fairlie and Meyer (2000) and Hout and Rosen (2000).

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Married: Married, not separated = 1, Single, divorced or separated =0.

Education: Dummy variables for (1) less than high school (2) high school only, (3) high school

plus college but no degree, and (4) college degree.

Children less than 18 years old in residence: Actual number.

Dependents outside of the family: Actual number.

Permanent Income: Income over the previous five years.

Permanent Income Square: The square of permanent income.

Health: Excellent or Good =1, Fair or Poor = 0.

Home ownership: Yes = 1, No = 0.

Received a gift/inheritance: The head or spouse (if any) received assets (cash or other) from an

inheritance. (Dummy variable).

Employment category: Dummy variables represent Entrepreneur, Worker, Retired, or

Unemployed.

Region: States are divided into 9 regions; See the Appendix for details. Dummy variables are

used, with the North Atlantic region being the reference region.

Previous research has shown that in regressions that pool both black and white families,

the binary variable Black is negative and statistically significant in predicting family wealth. I

will avoid redundancy and report only regressions that reflect the issues at hand: the impact of

entrepreneurship on the wealth of black and white families. Thus the regressions here predict

wealth for 1) black families and white families separately, with entrepreneurship as a predictor

variable, 10 and 2) entrepreneurs and workers separately, with race as a predictor variable. Both

10 A reviewer has correctly observed that if wealth (through minimum capital requirements and liquidity constraints) affects entry into entrepreneurship, then there is a simultaneous relationship between wealth and entrepreneurship: Using entrepreneurship as an independent variable in predicting wealth will make the coefficients inefficient. However, the degree to which wealth impacts business entry is unsettled. Holtz-Eakin, et al. (1994) and others provide evidence supporting this relationship, but Hurst and Lusardi (2001) provide strong evidence that it does not. Meyer (1990), who focused on black entrepreneurs, uses several data sets and does not find any evidence that financial resources play a roles in explaining the transition to entrepreneurship. Dunn and Holtz-Eakin (1995) find some but overall weak evidence that wealth affects entrepreneurship among the young. My purpose is to provide evidence on the empirical

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mean regressions (OLS) and median (quantile) regressions are reported, so that expression (1) can

represent the conditional mean or the conditional median regression function. Median

regressions are of interest if one is concerned about the median of wealth for families with a set of

characteristics. In addition, the skewness and fat tails of the wealth distribution may make the

median more relevant that the mean, which is focus of the OLS regression. I will also examine

the predictive content of the models using a slightly modified regression decomposition (Blinder

(1973), Oaxaca (1973)) that allows for median regression models.11

Table 2 shows the results of the OLS regressions predicting the amount of

wealth separately for white families and black families, and for entrepreneurs and workers. [Place

Table 2 here] The years observed are 1984, 1989 and 1994.12 The results of the black and white

family models show that with the exception to be noted, for both black and white families, age,

education, marriage, home ownership, good health, and the receipt of an inheritance are

positively associated with wealth. The exception is the median regression for black families for

which marriage is positive but has a negligible t-value. The number of children has a consistently

negative impact on wealth.

In both the OLS and median regressions, both black and white entrepreneurs hold higher

wealth than workers in their groups. The wealth advantage for white entrepreneurs over white

workers is $226,382 and $108,285 for the mean and median regressions, respectively. The

advantage of black entrepreneurs over black workers is $64,506 and $27,244. A Chow test of the

difference between white families and black families in the entrepreneurship coefficient is

statistically significant at the 0.01 level: The absolute wealth advantage of white entrepreneurs

over white workers is larger than that of black entrepreneurs over black workers.

relationship in a standard regression setting, thus I include entrepreneurship as a regressor in one set of the models that predict wealth. 11 The wealth decompositions for the median follow Altonji and Doraszelski (2001). 12 The weights for the 1999 data are not available in the format required for reliable estimates in greater detail. See the Appendix.

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Following Blinder (1973), Oaxaca (1973) and most similar studies, I measure the

difference in wealth between groups (the wealth gap) as the difference in the summary wealth

statistic for each group. Here the summary statistic includes both the mean and the median, the

latter resulting from the coefficients of the median regressions.13 The wealth gap is analyzed by

separating it into the portion attributable to differences in the average characteristics of the two

groups (education, age, marital status, etc.), the “explained” portion, and the portion attributable

to other influences, the “unexplained “ portion. One can use either the black or white coefficients

to calculate the explained and unexplained portions. I present results using both sets of

coefficients. In the decompositions, white families are projected to have higher wealth using both

sets of coefficients. Thus white families display higher wealth partly because they have more

traits associated with higher wealth than do black families. Typically, the more dissimilar the two

wealth functions, the larger the difference in the explained portion. The decompositions indicate

substantial differences between the wealth functions of black and white families, since the

explained portions of the mean and median regressions using the white family coefficients (99%

and 87%, respectively) are much larger than the explained portions using the black family

coefficients (38% and 32%). These results are consistent with Blau and Graham (1990).

Although the difference in wealth between white entrepreneurs and white workers is

larger than that between black entrepreneurs and black workers, not yet quantified are the wealth

differences between white entrepreneurs and black entrepreneurs compared to those between

white workers and black workers.14 In the mean and median regressions for entrepreneurs and

workers, Black has a negative sign, and for the median regression Black is not statistically

significant. The advantages for white entrepreneurs over black entrepreneurs are $113,000 and

13 In the mean regressions, the predicted wealth for each family i times the sample weight i summed over all i equals the mean wealth of each group. In general, this is not true for median regression models, so the sample median differs from the mean of the predicted medians. I use the predicted medians to measure the wealth gap in the median models. The results are similar if the group median is used.

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$42,000 in the mean and median regressions, while the advantages for white workers over black

workers are $14,000 and $3,000.

Age, home ownership, and the receipt of an inheritance are positively associated with

wealth for both entrepreneurs and workers. But while education is positively associated with

wealth for workers, it is not statistically significant for entrepreneurs. The same relationship

holds with the number of children. In the decompositions, entrepreneurs are projected to have

higher wealth using both sets of coefficients. Thus empirically entrepreneurs display higher

wealth in part because they have more traits associated with higher wealth than do workers. In

addition, more of the wealth gap between entrepreneurs and workers is explained by the

entrepreneurs’ coefficients than those of the workers for both the mean and median regressions.

This implies that these two groups have different wealth functions. But the difference in the

explained gap using the entrepreneurs/workers grouping is smaller than that using the white

families/black families grouping. This is evidence that the differences in the wealth functions by

race are larger than the differences in wealth functions by employment category.

To summarize, OLS and median regression models that predict wealth show that

controlling for education, age and other relevant variables, both white and black entrepreneurs

hold more wealth than the other categories (including workers) within their race groups, and the

absolute wealth advantage of white entrepreneurs over white workers is larger than that of black

entrepreneurs over black workers. While the wealth advantage of white workers over black

workers is statistically significant in the OLS regression and not statistically significant in the

median regression, the wealth advantage of white over black entrepreneurs is statistically

significant at the 0.10 level for both the OLS and median regressions.

14 Since (F1) WE* – WW* > BE* – BW*, it follows that (F2) WE* – BE* > WW* – BW*, where W, B, W*and E* white, black, worker and entrepreneur, respectively. But the left and right side of F2 may be + and +, – and –, or + and –. It is of interest to determine which of the three relationships holds.

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III. Do Black and White Entrepreneurs Achieve More Upward Wealth Mobility?

The focus of this section is whether entrepreneurs achieve more favorable wealth

mobility than workers. The theoretical influences leading to higher or lower wealth changes for

entrepreneurs compared to those for workers are discussed in Part II. Conceptually, in seeking

the best outcome, a person will stay a worker or entrepreneur or, subject to barriers, switch to the

other status. I examine the empirical outcomes of these choices by using the PSID data to follow

the employment status and changes in wealth of entrepreneurs and workers over the 1984-89 and

1989-94 periods.15 The PSID wealth data reflect a long-term panel with annual reinterview rates

in the range of 97 to 98 percent. Thus wealth changes for individual families can be directly

examined over an extended period.16

Two analyses are conducted. First, I derive transition matrices, as does Quadrini (1998,

1999), except that I separate entrepreneurs and workers into the black and white categories.

Second, I proceed in a more theoretically sound manner by using logistic regressions to observe

the wealth transitions of entrepreneurs and workers, while controlling for other variables that

effect changes in wealth. Table 3 reports the wealth transition matrices of four sub-samples of

both the black and white entrepreneurs and workers. [Place Table 3 here] “Staying workers”

(“staying entrepreneurs”) started and ended the five-year period as workers (entrepreneurs); and

“switching workers” (“switching entrepreneurs”) moved from worker (entrepreneur) to

entrepreneur (worker) over the period. The families of the four groups have been divided

according to the families’ wealth ranks at the start and end of each five-year period. A family

may start or end in the top third, middle third or bottom third of the entire wealth distribution.

15 Continuing entrepreneurs in 1989-94, for example, can include entrepreneurs who had failed and switched to another entrepreneurial venture by 1989, workers who had “failed” (released by their employers) and started their own business by 1989, and entrepreneurs who failed during 1989-94 but remained in entrepreneurship (switched to another venture, etc.). Continuing workers also includes corresponding failures and successes. The tests determine whether entrepreneurs or workers—in light of both successes and failures within the groups—perform better in wealth mobility after controlling for relevant variables; and if the results are consistent for both black and white entrepreneurs and workers. 16 The PSID follows young adults as they leave home and form their own families. In this way, the panel regenerates a new sample and, with weights, can provide national estimate of income, wealth and saving.

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Note that the wealth ranks reported are based upon all families, not just workers and

entrepreneurs. Each employment category (e.g. staying workers) has three rows that refer to the

families that started in the bottom, middle and top third. The three columns represent the percent

of the families that started in that row (e.g. bottom third) that ended the five-year period in the

bottom third, middle third or top third. In the case of those who started each five-year period as

workers, the following relationships obtain:

a) Bottom third: For both the white and black groups, the fraction of families moving to a higher

tier is greater for the workers that moved into entrepreneurship than for staying workers. A

higher fraction of white switching workers than black switching workers moved to a higher tier.

b) Middle third: For both black and white workers the outcomes for workers moving into

entrepreneurship are more favorable than the outcomes of staying workers. The percentage of

switching workers that moved up is higher and that moved down is lower for the white workers

than for the black workers.

c) Top third: For both the black and white groups, the percentage of families falling to lower tiers

is smaller for switching than for other worker families. However, the outcomes for the white

switching workers are more favorable than for the black switching workers.

In the case of those that started as entrepreneurs, the following relationships hold.

a) Bottom third: For both the black and white groups, the percentage of staying entrepreneurs

that moved to a higher tier is greater than is the percentage for switching entrepreneurs. The

percentage of white staying entrepreneurs that moved to a higher tier is greater than that of the

black entrepreneurs.

b) Middle third: For both black and white groups, the percentage of upwardly mobile families is

higher for the staying entrepreneurs than for the switching entrepreneurs. Compared to the black

entrepreneurs, the percentage of white staying entrepreneurs that moved to the top tier is higher

and that fell to the bottom tier is lower.

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c) Top third: For both black and white entrepreneurs the percentage of families falling to a lower

tier is smaller for staying entrepreneurs than for switching entrepreneurs. A lower fraction of the

white entrepreneurs fell to a lower tier than did the black entrepreneurs.

These results demonstrate that a higher (lower) fraction of continuing and new workers

stay in or move to lower (higher) wealth positions than continuing and new entrepreneurs. The

advantage of entrepreneurship holds for both black and white families. In addition, a higher

(lower) fraction of continuing and new white entrepreneurs stay in or move to higher (lower)

wealth positions than continuing and new black entrepreneurs. However, one should is

determine the extent that these relationships hold when controlling for variables that ordinarily

effect wealth transition. In order to test the statistical significance of the effect of race and

entrepreneurship on wealth mobility, I estimate a logistic model using the same variables

included in Table 2. Two models are estimated: P(1) = Prob ( Family above the bottom tier falls

into the bottom tier) = F (Independent variables); and P(2) = Prob(Family below the top tier

moves into the top tier) = F(Independent variables). The independent variables include the race

of the family, its involvement in entrepreneurship, and the other independent variables. Table 4

shows the logistic regressions of the entrepreneurs and workers for 1984-89 and 1989-94. [Place

Table 4 here] The reference work category is Staying Entrepreneurs. Interaction terms for Black

and the other three work categories are also included.

The P(1) logistic regression takes those families that were above the bottom third of the

wealth distribution at the start of the period (e.g. 1984) and utilizes the independent variables to

estimate the odds that the family falls into the bottom third of the wealth distribution by the end

of the period (e.g. 1989). Positive coefficients indicate that more of that variable leads to a

higher probability of falling into the bottom third from the middle or upper third. The P(1)

regression coefficients generally show the expected signs: more education, higher age, male,

married, good health, home ownership, and receipt of a gift/inheritance reduce the probability of

the family’s wealth falling into the bottom tier of the wealth distribution. Having more children

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and the single female status (compared to the single male reference) increase the probability of

the family’s wealth falling into the bottom third. Staying Entrepreneurs is the reference work

category, and the coefficients of the other three categories are positive. Thus relative to Staying

Entrepreneurs, each of the other categories increases the probability of the family’s wealth falling

into the bottom tier. Each of the interaction terms is positive. This indicates that being black

(instead of white) in each of these work categories increases the probability of wealth falling into

the bottom third. Given how this regression model and its interaction terms are constructed, the

coefficient labeled “Black” represents the difference in the effect on P(1) between black staying

entrepreneurs and white staying entrepreneurs, controlling for the effect of the other variables.

The negative sign for Black indicates that when the other variables are controlled for, black

staying entrepreneurs have a lower probability of wealth falling into the bottom tier. This

coefficient is statistically significant at the 0.05 level.

The logistic model for P(2) estimates the odds that a family below the top third in the

wealth distribution at the start of the five-year period is in the top third at the end of the period.

Positive coefficients indicate that more of that variable leads to a higher probability of moving

into the upper third from the middle or lower third. The variables representing personal

characteristics have the appropriate signs. Here the coefficients reflecting the other work

categories are negative, indicating that relative to staying entrepreneurs, being in these other

categories reduces the probability of the family moving into the top third of wealth. The

interaction terms are not statistically significant, indicating that race does not matter for the three

work categories in moving into the top tier of wealth. The Black coefficient is negative, but not

statistically significant. Thus when controlling for the other variables in the model, being a black

staying entrepreneur instead of a white staying entrepreneur does not effect the probability of

moving into the top tier of wealth.

To summarize, the transition matrices show that both black and white entrepreneurs who

remain in business have more upward mobility and less downward mobility in the wealth

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distribution than those in their respective races who remain workers. The transition matrices also

show that white entrepreneurs have more upward mobility and less downward mobility than

black entrepreneurs. The logistic regression models, which control for variables such as

education, age and receipt of a gift or inheritance, reinforce that staying entrepreneurs have more

upward mobility and less downward mobility in the wealth distribution than staying workers.

Thus the traits that characterize entrepreneurs are shown to result in more upward mobility for

both black and white entrepreneurs. The logistic regressions also show that black staying

entrepreneurs display less downward mobility than white staying entrepreneurs, and that the

difference in upward mobility between black staying entrepreneurs and white staying

entrepreneurs is not statistically significant.

IV. Do Black and White Entrepreneurs Have Higher Wealth-Income Ratios and Savings Rates?

The greater wealth of business families relative to worker families would be less

interesting if business families also earn more income (in proportion to wealth). To what extent

are the wealth-income ratios of entrepreneurs also higher than those of workers? Figure 1 shows

the average per-family wealth of black and white workers and entrepreneurs in each income

decile, as a percentage of total per-family wealth. [Place Figure 1 here] The deciles are

calculated based on the entire population (including retired and unemployed). The mean family

wealth of entrepreneurs is higher than that of workers in every income group. This relationship

holds for both black and white entrepreneurs in their respective racial groups.

In order to test the statistical significance of the differences in the wealth-income ratio of

entrepreneurs and workers, I compute OLS and median regressions using the independent

variables specified above, augmented by family wealth as an independent variable. I combine the

data for 1984, 1989 and 1994, and use time indicator variables. Table 5 provides the results of

the regressions, which are performed on the combined black and white workers and entrepreneurs

(model 1), black families (model 2), white families (3), entrepreneurs (4) and workers (5). [Place

18

Table 5 here] The values of R2 (adjusted R2 for OLS and pseudo R2 for the median regression)

range from 0.31 to 0.54. Model 1 for both types of regression shows that the wealth-income

ratios of blacks families are lower than those of white families, and the wealth-income ratios of

workers are lower than those of entrepreneurs. In model 2, the wealth-income ratios of black

entrepreneurs are not statistically different than those of black workers in the OLS regression, but

the black entrepreneurs have lower wealth-income ratios than black workers at the 0.01 level in

the median regression. In contrast, for model 3, the wealth income ratios of white entrepreneurs

are found to exceed those of white workers in both types of regression. In model 4, the wealth-

income ratios of the black entrepreneurs are less than those of the white entrepreneurs, for both

types of regression. In model 5, the difference in the wealth-income ratios between black

workers and white workers is not statistically significant in the OLS regression, but the black

workers’ ratios are lower at the 0.01 level in the median regression. I also observe that the black

group appears to have structural differences in how the personal variables associate with the

wealth-income ratio. While for the black group the coefficients of the old age category and

college degree category are negative (the latter significant at the 0.10 level), both are it positive

and statistically significant for the white group.

The upward mobility in the wealth distribution (and wealth-income ratios) of continuing

and entering entrepreneurs, and the downward mobility of households that exit entrepreneurship,

suggest that entrepreneurship is related to household saving. In defining saving, I take a broad

definition of family wealth to capture the relationship of entrepreneurship to both business and

nonbusiness saving. That is, I define the saving rate as the change in wealth (e.g. 1994 wealth

minus 1989 wealth) divided by the income from the starting year to the ending year (e.g., 1989

through 1993). The definition of saving here includes the changes in the market value of assets

arising from both passive and active saving, that is, returns on prior saving and current net

contributions to savings. I sum active and passive saving to reflect that rational and informed

19

families consider the financial impact of both as they allocate their resources.17 I use the income

from the start to the end as a proxy for permanent family income.18

Table 6 displays the mean saving rate of black and white families, along with descriptive

variables describing both sets of families. [Place Table 6 here] The statistics summarize two

time periods: 1984-89 and 1989-94. The families described have the same head of household

over each five-year period, and those who were either workers or entrepreneurs over the period.

The mean saving rate of the black families is 5.4%, compared to 8.8% for the white families. The

black families are characterized by the younger age, poorer health, lower rate of home ownership,

and the lower propensity to receive a gift or inheritance. As noted earlier, the black families also

have a lower rate of entrepreneurship than the white families. Table 7 shows the results of OLS

and median regressions of the saving rate as a function of the independent variables used earlier,

including the entrepreneurial transitions category. [Place Table 7 here] The dichotomous

entrepreneurial categories utilize staying workers as the reference category.

The consistent relationship in Table 7 is the higher saving rates of both black and white

staying entrepreneurs.19 The coefficient for this category is statistically significant in both the

17 Juster, Smith and Stafford (1999) discuss the issues of using these and other available survey data to measure saving rates. 18 Alternatively, one could estimate permanent income as a function of family demographics and use predicted income as a measure of permanent income. As observed by Gentry and Hubbard (2000, p. 30), this method has two problems in estimating the association between entrepreneurship and saving. First, entrepreneurship likely entails unobservable differences in talent that would not be captured by a regression that estimates permanent income. By using predicted income for the family, one ignores the unobservable talent that is reflected by current income. Second, many of the variables that are likely candidates to predict permanent income (age, experience, education, etc.) may have independent effects on saving decisions. 19 One possible explanation for the higher saving rate for entrepreneurs over workers is that some workers may be covered by the employers’ pension plans, while entrepreneurs must save for their retirement in their personal assets. Data available only in 1984 provide crude information to test this relationship. In 1984 there were extensive questions on pension plans of the respondents, including: 1. Are you covered by a pension or retirement plan on your present job? 2. Have you worked under the main or basic plan long enough to earn the right to be vested? 3. How are the benefits for your pension determined—by a definite formula based on years of service or salary, or by the amount of money in your account, or both ways? 4. What is the approximate dollar amount in your retirement benefit account right now? 5. How much could you take out of this account today if you were to leave this employer? I tested five regression models for 1984-89 that augmented model 1 in Table 7 by an independent variable reflecting the answers to these questions: Model 1a: I added the binary variable 1= No, 0 = yes to pension plan coverage. Model 1b: 1= not vested, 0 = vested. Model 1c: 1 = no pension plan, 0 = yes for defined benefit or combination pension

20

OLS and median regressions at the 0.01 level in model 1, the regression that combines black and

white families, and in models 2 and 3, the regressions of black families and white families,

respectively. In both the OLS and median models, the saving rates for workers switching to

entrepreneurship are higher than for staying workers in model 1. While these relationships carry

over to the individual median regressions for models 2 and 3, the OLS coefficients for switching

workers of black families (model 2) are not statistically significant, and for white families (model

3) the OLS coefficient is statistically significant only at the 0.10 level. In model 1, the coefficient

for Black is small in absolute terms, and not statistically significant. This indicates that race does

not matter in predicting saving rates as measured here, when the effect of the other variables are

considered. The saving rate generally increases with income in all of the regressions, particularly

in the higher income levels. But the black families display less of this relationship than the white

families.20 Age and education are not found to be statistically significant in the OLS regressions

of model 1, while both are statistically significant in the median regression of model 1. This

difference holds because age and education are significant predictors for white families but not

black families in model 1 for the median regressions. Marriage is not found to be statistically

significant in these regressions. In both types of regressions of model 4 (continuing

entrepreneurs), race is not statistically significant in predicting the saving rate for entrepreneurs.

However, in model 5, for staying workers race is not statistically significant in the OLS

regression, while the median regression indicates that black workers save less than white workers

at the 0.01 level.

plan. Model 1d: dollar amount in the retirement account, and Model 1e: dollar amount that could be taken out now. Each of the five regression coefficients was positive, and the coefficients for 1a, 1b and 1c were statistically significant, while 1d and 1e were not. Tests showed that the coefficient for staying entrepreneurs was larger than each of la, lb and 1c at the 0.01 level. The conclusion is that using these measures, the saving rate of entrepreneurs is higher than that of workers with no pensions. Gentry and Hubbard also concluded that the differences between entrepreneurs and workers in saving rates were quite large relative to reasonable estimates of contribution rates for pensions. 20 I also conducted regressions that calculated the savings rate as the change in wealth divided by five (the average change in wealth), divided by the average of the income in the first year and the last year of the five-year period. This is the measure used by Gentry and Hubbard. The results using the Gentry-Hubbard are consistent with the results reported here.

21

In summary, univariate comparisons show that the wealth-income ratios of both black

and white entrepreneurs are higher than those of workers in their racial groups, and the wealth-

income ratios of white entrepreneurs are higher than those of black entrepreneurs. The

regressions confirm that white entrepreneurs have higher wealth-income ratios than white

workers, and that white entrepreneurs have higher wealth-income ratios than the black

entrepreneurs. But the regressions also show that black entrepreneurs have the same (OLS

regression) or lower (median regression) wealth-income ratios than black workers. The results

for saving rates differ somewhat. The saving rates of black and white continuing entrepreneurs

are higher than those of black and white workers, respectively, and the black and white

entrepreneurs’ saving rates are not found to differ at statistically significant levels.

V. Overview and Discussion

A. Overview

In the U.S., wealth is disproportionately distributed towards white families in relation to

black families. Among both black and white families, wealth is disproportionately distributed

towards entrepreneurs instead of workers. However, black entrepreneurs hold a lower fraction of

black wealth than white entrepreneurs hold of white wealth, because of the lower rate of

entrepreneurship among blacks. Both black and white entrepreneurs have more upward mobility

and less downward mobility in the wealth distribution than black and white workers, and ignoring

other variables that effect wealth mobility, black entrepreneurs have less upward and more

downward mobility than white entrepreneurs. If one controls for variables that would ordinarily

affect wealth mobility, then black entrepreneurs and white entrepreneurs have the same upward

mobility and black entrepreneurs less downward mobility in the wealth distribution. The saving

rate of entrepreneurs is higher than that of workers, and the black and white entrepreneurs’ saving

rates are not found to differ when I control for other variables that are associated with saving

rates.

22

B. Discussion

Can entrepreneurship among black families significantly reduce the gap in wealth

between black and white families in the U.S.? Although I find that existing black entrepreneurs

have higher wealth, higher savings rates and more upward wealth mobility than black workers, I

do not show how and if these traits would hold for new black entrepreneurs if the rate of black

entrepreneurship increases. Further research is needed to quantify the causes and consistency of

these traits. In addition, I find neither the wealth-income ratios nor the saving rates of black

entrepreneurs are higher than those of white entrepreneurs. Thus, given a much higher starting

wealth of the white entrepreneurs, the disparity between black and white entrepreneurs in the U.S.

will continue in the future unless significant acts occur that provide wealth increasing advantages

to black entrepreneurs. Of course, “affirmative action” programs for this purpose (such as

government set-asides) have existed in various state and local governments and the U.S. federal

government since the 1970s. Many of these programs have been criticized as unfair and

ineffective (and have been made illegal in some jurisdictions), and are now being reduced or

eliminated in many government jurisdictions, including the U.S. government (see Bates (1995)

and Ruffin (1999)). Private sector programs that enhance black entrepreneurship have not had

nor are expected to have a significant impact in this arena.

The entrepreneurship rate of black families is about one-third that of white families, and

this ratio remained roughly constant over the 20th Century (Fairlie and Meyer, 2000).

Realistically, it will be a significant challenge simply to increase the rate of entrepreneurship

among black families to two-thirds that of white families; and given the size of the wealth

disparity and the uncertainty about the characteristics of new black entrepreneurs, it is highly

uncertain that they would significantly reduce the gap in wealth between black and white

families. However, one can argue (for example, see Oliver and Shapiro, 1995) that black

entrepreneurship should be supported simply because past acts have restricted black Americans

23

from participating equitably in the U.S. economy, and the opportunity to participate and achieve

should be fully available to them as well as other U.S. citizens.

24

APPENDIX

A. Definition of Wealth

Wealth includes real estate (own or main home, second home, rental real estate, land contract

holdings), cars, trucks, motor homes, boats, farm or business, stocks, bonds, mutual funds,

savings and checking accounts, money market funds, certificates of deposit, government savings

s bonds, Treasury bills, IRAs, bond funds, cash value of life insurance policies, valuable

collections for investment purposes, and rights in trust or estate, less mortgage, credit card, and

other debt on such assets. This measure does not include wealth in the form of private pensions

or expected social security retirement benefits. The total sample is representative of the U.S.

population when sample weights provided by the PSID are used. Through funding from the

Survey of Economic Opportunity the data set over-samples lower-income and black American

families.21 I use the 1984, 1989, 1994 wealth data in the most detailed analyses for this study. I

do not examine the 1999 data in as much detail, since they are not sufficiently refined for the

more detailed analyses.22

B. Self-employment in PSID

For a detailed description of the Panel Study of Income Dynamics, see Hurst, Luoh and Stafford

(1998). The wealth data used in this study comes from the supplement on household family

wealth funded by the National Institute on Aging. The information on Employment Category and

Self-employment is based upon questions asked during the interviews. The categories I use are

based upon the possible answers as reported by the PSID. The first question asked which relates

to defining employment categories is: “We would like to know about what you do—are you

21 These data contain essentially only black and white families. Originally, other ethnic groups (Latinos, Asian, and native Americans) were not to be represented in the sample. 22 The 1999 data are the early release version. Family weights for 1999 were unavailable and we used 1997 family weights. This essentially means that the 1999 wealth data statistics are for families with the same head in 1997 and 1999.

25

(Head) working now, looking for work, retired, keeping house, a student, or what? The PSID

categorized the respondent ‘s answer as follows:

1. Working now

2. Only temporarily laid off

3. Looking for work, unemployed

4. Retired

5. Permanently disabled; temporarily disabled

6. Keeping house

7. Student

8. Other

I categorize those answering 1 as either entrepreneurs or wage/salary depending on how the

question below was answered. Responses 2, 3, 6, 7 and 8 were categorized as “unemployed”.

Answers 4 and 5 were categorized as “retired”. For those answering “Working now”, they are

then asked “Do you work for someone else, yourself, or what?’

The possible answers from this question are:

1. Someone else

2. Both someone else and self

3. Self only

Those responding as 1 or 2 were placed into the wage/salary category. There were

relatively few answering 2. I conducted tests including 2 in 1 both 1 and 3, and found

very little difference in our results.

C. Regional Specifications of the States.

1. New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont.

2. North Atlantic (excluded region): New Jersey, New York, Pennsylvania.

26

3. Southeast: Delaware, District of Columbia, Florida, Georgia, Maryland, North Carolina,

South Carolina, Virginia, West Virginia.

4. East South Central: Alabama, Kentucky, Mississippi, Tennessee.

5. Oil States: Arkansas, Louisiana, Oklahoma, Texas.

6. Plains States: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota.

7. Mountain States: Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah,

Wyoming.

8. West: Alaska, California, Hawaii, Oregon, Washington.

9. Great Lakes: Illinois, Indiana, Michigan, Ohio, Wisconsin.

27

REFERENCES

Altonji, J. and U. Doraszelski , “The Role of Permanent Income and Demographics in

Black/White Differences in Wealth”, NBER Working Paper No. 8473,2000. ________, F. Hayashi and L. Kotlikoff , “Parental Altruism and Inter Vivos Transfers:

Theory and Evidence”, Journal of Political Economy Vol. 105, 1121-1166, 1997. Aronson, Robert L., Self-employment: A Labor Market Perspective, Ithaca, NY: ILR Press,

Cornell University, 1991. Bates, Timothy, Race, Self-employment and Upward Mobility,Baltimore: The Johns

Hopkins University Press, 1997. ____________,“Why Do Minority Business Development Programs Generate So Little

Minority Business Development?” Economic Development Quarterly, Vol. 9, 3-14, 1995. Boston, Thomas, Affirmative Action and Black Entrepreneurship, New York: Routledge, 1999. Blau, Francine D. and John W. Graham, “Black-White Differences in Wealth and Asset

Composition”, Quarterly Journal of Economics, Vol. 105, 321-339, 1990. Butler, John S., Entrepreneurship and Self-Help Among Black Americans, Albany, NY:

State University of New York Press, 1991. Carroll, Christopher D., “How Does Future Income Affect Current Consumption?”,

Quarterly Journal of Economics, Vol. 109, 111-147, 1994 Devine, Theresa J., “Characteristics of Entrepreneurs Women in the United States”

Monthly Labor Review, Vol. 117, 20-34, 1994. Dunn, Thomas and Douglas Holtz-Eakin, “Capital Market Constraints, Parental Wealth and the

Transition to Self-Employment among Men and Women, Discussion Paper, Bureau of Labor Statistics, 1995.

Evans, D.S. and Jovanovic, B., “An Estimated Model of Entrepreneurial Choice Under Liquidity

Constraints”, Journal of Political Economy, Vol. 97, 808-827, 1989. Fairlie, Robert W., “The Absence of the African-American Owned Business: An Analysis

of the Dynamics of Self-Employment”, Journal of Labor Economics, Vol. 17, 80-108, 1999.

_____________ and Bruce D. Meyer, “Trends in Self-employment among White and Black Men

During the Twentieth Century”, Journal of Human Resources, Vol. 35, 643-669, 2000. Ferber, Marriane A. and J. Waldfogel, “The Long-term Consequences of Nontraditional

Employment”, Monthly Labor Review, Vol. 121, 3-12, 1998. Gentry, William and R. Glenn Hubbard, “Entrepreneurship and Household Saving”, NBER

Working Paper No. 7894, 2000.

28

Hamilton, Barton H., “Does Entrepreneurship Pay? An Empirical Analysis of the Returns to Self-Employment”, Journal of Political Economy Vol. 108, 604-631, 2000.

Harmon, J. A. Lindsay and Carter Woodson, The Negro as a Businessman, College Park, MD:

McGrath, 1929. Holtz-Eakin, Douglas, David Joulfaian and Harvey Rosen, “Sticking It Out: Entrepreneurial

Survival and Liquidity Constraints”, Journal of Political Economy, Vol. 102, 53-75, 1994.

Hout, Michael and Harvey S. Rosen, “Self-employment, Family Background, and Race”, Journal

of Human Resources, Vol. 35, 670-692, 2000. Hurst, Erik, Ming Ching Luoh, and Frank P. Stafford , “The Wealth Dynamics of American

Families, 1984-1994,” Brookings Papers on Economic Activity,1998:1, 267-329, 1998. _________ and Annamaria Lusardi, “Liquidity Constraints, Wealth Accumulation, and

Entrepreneurship”, Working Paper, University of Chicago Business School, 2001. Jovanovic, Boyan, “Selection and the Evolution of Industry”, Econometrica, Vol. 50, 649-670,

1982. Juster, F., James P. Smith and Frank P. Stafford, “The Measurement and Structure of Household

Wealth” Labor Economics, Vol. 6, pp. 253-275,1999. Kihlstrom, Richard and Jean-Jacques Laffont, “A General Equilibrium Entrepreneurial Theory of

Firm Formation Based on Risk Aversion”, Journal of Political Economy, Vol. 87, 719-748, 1979.

Lucas, Robert , “On the Size Distribution of Firms”, Bell Journal of Economics, Vol.9, 508-523,

1978. Menchik, Paul L. and Nancy A. Jianakoplos, “Black-White Wealth Inequality: Is Inheritance the

Reason?, Economic Inquiry Vol. 35, 428-442, 1997. Oliver, Melvin L. and Thomas M. Shapiro, Black Wealth/White Wealth: A New Perspective on

Racial Inequality, New York: Routledge, 1995. Quadrini, Vicenzo, “The Importance of Entrepreneurship for Wealth Concentration and

Mobility,” The Review of Income and Wealth, Series 45, 1-19, March 1999. ______________, “Entrepreneurship, Saving, and Social Mobility”, Review of Economic

Dynamics, Vol. 3, 1-40, 2000. Ruffin, David C., “Assault Continues on Set-Asides”, Focus, Vol. 27, 7, 1999. Wallace, Robert L., Black Wealth Through Entrepreneurship, Englewood, Maryland: Duncan

and Duncan, Inc., 1993. Wolff, Edward N., “Recent Trends in the Size Distribution of Household Wealth” Journal of

Economic Perspectives, Vol. 12, 131-150, 1998.

29

TABLE 1

Family Wealth, White and Black Families, by Employment Status

in 1999 Dollars

1984 White Families Black Families

Group Entrpnrs Workers Retired Unemp Group Entrpnrs Workers Retired Unemp

Mean 176,954 473,280 133,864 215,005 64,533 30,633 94,861 34,091 40,823 10,788

Median 61,733 214,864 47,318 105,508 16,997 3,848 40,087 9,621 13,950 0

N 4,341 469 2,783 677 399 2,576 74 1,509 384 609

Subgroup to Grp Mean 1.00 2.67 0.76 1.22 0.36 1.00 3.10 1.11 1.33 0.35

Subgroup to Grp Median 1.00 3.48 0.77 1.71 0.28 1.00 10.42 2.50 3.63 0.00

% of Group Population 100.0 10.61 60.31 19.25 9.83 100.0 2.35 60.67 18.47 18.50

% of Group Wealth 100.0 28.55 45.30 22.51 3.64 100.0 6.35 56.78 29.97 6.90

Black/White Ratio

Mean 0.17 0.20 0.25 0.19 0.17

Median 0.06 0.19 0.20 0.13 0.00



Mean 193,891 609,651 115,950 230,377 85,596 39,750 144,031 37,916 66,801 8,157

Median 63,147 251,244 44,337 122,263 16,123 6,583 71,208 8,061 23,781 0

N 4,475 588 2,924 695 268 2,608 103 1,625 409 471


Subgroup to Grp Median 1 3.98 0.7 1.94 0.26 1.00 10.82 1.22 3.61 0.00


% of Group Wealth 100.0 38.16 36.35 22.37 3.12 100.0 8.82 65.46 22.98 2.74

Black/White Ratio

Mean 0.21 0.24 0.33 0.29 0.10

Median 0.10 0.28 0.18 0.19 0.00



Mean 198,499 500,242 140,580 227,342 88,913 38,758 104,686 36,268 62,894 14,453

Median 69,698 202,348 49,463 121,409 13,490 8,431 67,449 10,679 20,235 0

N 4,699 618 2,976 818 287 2,610 112 1,614 436 448


Subgroup to Grp Median 1.00 2.90 0.71 1.74 0.19 1.00 8.00 1.27 2.40 0.00


% of Group Wealth 100.0 31.18 42.18 23.67 2.97 100.0 13.73 58.67 23.75 3.84

Black/White Ratio

Mean 0.20 0.21 0.26 0.28 0.16

Median 0.12 0.33 0.22 0.17 0.00

Source: Author's calculations using data from the PSID Supplemental Wealth Files.

30

TABLE 2: REGRESSIONS EXPLAINING FAMILY WEALTH Ordinary Least Squares Regressions Median Regressions Dependent Variable: Real Wealth ($1999) White Families Black Families White Families Black Families Indep. Variables# Coef. t-value Coef. t-value Coef. t-value Coef. t-value Intercept -224,535 -13.3 *** -41,785 -6.6 *** -76,803 -9.6 *** -7,047 -659.0 ***Age < 35 (Young) -27,540 -3.9 *** -4,713 -1.9 * -13,585 -4.5 *** -412 -100.9 ***Age > 54 (Old) 100,432 13.0 *** 33,733 10.9 *** 49,157 13.4 *** 4,923 1,005.1 ***Education (Years) 7,267 6.9 *** 3,471 8.3 *** 4,156 8.5 *** 539 797.2 ***No. of Children -13,148 -4.6 *** -2,263 -2.6 *** -9,478 -7.8 *** -267 -207.9 ***No. of Deps Outside 15,087 1.8 * -114 -0.1 3,797 1.0 -119 -26.2 ***Male 3,054 0.3 7,153 2.5 ** 3,845 0.8 1,273 265.4 ***Married 23,108 2.7 *** 7,770 2.3 ** 16,017 4.0 *** 0.6113 0.1 Health Exc or Good 35,402 4.6 *** 3,229 1.3 16,474 4.5 *** 1,151 283.0 ***Own Home 82,239 12.2 *** 42,468 17.7 *** 60,179 19.4 *** 36,279 9,160.7 ***Retired 100,173 10.8 *** -9,563 -2.6 *** 22,986 5.1 *** 214 38.1 ***Entrepreneur 226,382 29.4 *** 64,506 11.2 *** 108,285 31.4 *** 27,244 3,407.6 ***Unemployed 58,770 4.8 *** -2,058 -0.7 11,431 2.0 ** 313 64.4 ***Permanent Income 0.5029 13.8 *** -0.2557 -8.4 *** -0.0004 0.0 -0.0264 -537.3 ***Perm. Inc. Squared 1.11E-07 3.5 *** 1.11E-06 18.1 *** 6.19E-07 43.2 *** 3.49E-07 3,624.8 ***Inheritance Received 63,571 10.2 *** 24,759 6.0 *** 37,001 12.9 *** -597 -94.0 *** N 9,327 5,235 9,327 5,235 Adjusted R2 0.3319 0.3643 Psdo R2 0.2357 0.2656 Regression Decompositions of the Race Gap in Wealth Ordinary Least Squares Regressions Median Regressions White Coefficients Wealth Estimates Wealth Estimates A. White traits 176,422 118,619 B. Black traits 33,725 33,626 Black Coefficients C. Black traits 32,526 21,329 D. White traits 86,455 51,966 E. Total Gap: A - C 143,896 97,290 Explained Gap Using White Coeff.: A - B 142,697 84,993 % (A - B)/E 99.2 87.4 Explained Gap Using Black Coeff.: D - C 53,929 30,637 %(D - C)/E 37.5 31.5

31TABLE 2: REGRESSIONS EXPLAINING FAMILY WEALTH (CON.)

Ordinary Least Squares Regressions Median Regressions

Dependent Variable: Real Wealth ($1999)

Entrepreneurs Workers Entrepreneurs WorkersIndep. Variables# Coef. t-value Coef. t-value Coef. t-value Coef. t-value

Intercept -115,174 -1.5 -95,678 -7.8 *** -78,059 -2.2 ** -14,426 -3.6 ***Black -113,386 -1.9 * -13,874 -2.5 ** -41,989 -2.0 * -2,602 -1.5Age: Young ( < 35) -83,850 -2.7 *** -23,954 -5.5 *** -38,137 -3.0 *** -10,457 -8.0 *** Old (> 54) 176,308 6.1 *** 84,110 16.1 *** 138,091 10.4 *** 47,769 27.1 ***Education (Years) -3,402 -0.8 4,347 5.6 *** -822 -0.4 1,681 6.7 ***No. of Children -15,910 -1.5 -5,507 -3.1 *** -6,824 -1.5 -3,810 -7.2 ***No of other dependents 8,003 0.3 5,767 1.3 -20,362 -1.5 227 0.1Male Head 41,985 0.9 -9,369 -1.6 21,272 0.9 -27 0.0Married Couple 11,048 0.3 2,435 0.4 12,269 0.8 4,722 2.5 **Health Exc or Good 21,557 0.6 4,646 0.8 8,120 0.5 4,138 2.1 **Own Home 153,092 5.0 *** 55,524 12.4 *** 113,855 8.4 *** 43,392 30.3 ***Permanent Income 1.4541 11.9 *** 0.3589 12.7 *** 1.0261 19.1 *** -0.1007 -11.0 ***Perm. Inc Squared -4.58E-07 -5.3 *** 2.30E-07 8.9 *** -2.66E-07 -7.4 *** 7.98E-07 93.6 ***Inheritance Received 75,503 3.1 *** 54,691 11.7 *** 35,732 3.4 *** 25,437 17.1 ***

N 1,543 9,327 1,543 9,327Adjusted R2 0.3163 0.3208 Psdo R2 0.2014 0.2678

Regression Decompositions of the Employment Category Gap in Wealth

Ordinary Least Squares Regressions Median RegressionsEntr. Coefficients Wealth Estimates Wealth Estimates A. Entr. traits 368,990 262,537 B. Worker traits 290,726 210,857Worker Coefficients C. Worker traits 113,567 77,523 D. Entr. traits 149,475 111,336E. Total Gap: A - C 255,423 185,014Explained Gap Using Entr. Coeff.: A - B 78,264 51,680 % (A - B)/E 30.6 27.9Explained Gap Using Worker Coeff.: D - C 35,908 33,813 %(D - C)/E 14.1 18.3

# Time and region dummies are also included as control variables in regressions.*** = .01 ** = .05 * = .10 Source: PSID Core and Supplemental Files, and the author's calculations.

32

Table 3: Five Year Transition Matrices for Changes in Wealth Position

Combined Results for 1984-89 and 1989-94

White Workers/Entrepreneurs

Ending Third: Ending Third:

Bottom Middle Top Bottom Middle Top

Staying Workers Switching Workers

Starting Third: Stat. Sig.#

Bottom 69.9 26.4 3.7 58.8 25.8 15.5 ***

Middle 17.7 63.5 18.8 16.8 44.3 38.9 ***

Top 2.4 21.7 76.0 1.2 13.8 85.1 **

Switching Entrepreneurs Staying Entrepreneurs

Bottom 72.1 18.6 9.3 31.4 42.9 25.7 ***

Middle 32.3 50.0 17.7 12.8 47.0 40.2 ***

Top 3.7 23.5 72.8 2.3 8.8 88.9 ***

Black Workers/Entrepreneurs

Ending Third: Ending Third:

Bottom Middle Top Bottom Middle Top

Staying Workers Switching Workers

Starting Third:

Bottom 81.4 17.1 1.5 48.0 44.0 8.0 ***

Middle 31.9 60.1 8.1 27.8 50.0 22.2 ***

Top 13.5 36.5 50.0 16.7 16.7 66.7 **

Switching Entrepreneurs Staying Entrepreneurs

Bottom 93.8 6.3 0.0 50.0 25.0 25.0 ***

Middle 50.0 50.0 0.0 12.5 68.8 18.8 **

Top 28.6 14.3 57.1 6.3 25.0 68.8 ***

*= .10 **=.05 ***=.01. #Statistical significance of row differences.

A chi-square or Fisher test was used to test the difference in the distribution of the rows. The latter was used

when the number of cases in one or more cells might result in a chi-square test being inappropriate.

The selected subsamples were categorized into three groups according to where the family ranked in the wealth

distribution of all families at the start of the five-year period. The starting third for 1984-89 is which third of the

wealth distribution the family ranked in 1984. The starting third for 1989-94 is determined by the family ranking

in 1989. The matrices show the relative mobility of the families rather than the absolute change in wealth.

Source: PSID core and supplemental wealth files and the author's calculations.

33

Table 4: Logistic Models Predicting Individual Family Transitions in the Wealth Distribution in the Five-Year Periods 1984-89 and 1989-94.

Probability that a family above the Probability that a family below bottom third falls into the bottom third the top third rises into the top third

Wald Chi- Stzd. Wald Chi- Stzd.Independent Variables# Coeff. Square Coeff. Independent Variables# Coeff. Square Coeff.

Intercept -1.9261 480.4 *** Intercept -2.3842 806.7 ***Black -1.0225 4.5 ** -0.6110 Black -0.2159 1.0 -0.1684Education: Education: H.S. only -0.4454 158.2 *** -0.5196 H.S. only 0.4369 99.7 *** 0.4714 Coll, no degree -0.8531 430.9 *** -0.8745 Coll, no degree 0.8686 370.2 *** 0.8223 Coll degree -1.3803 1,033.2 *** -1.6157 Coll degree 1.2614 831.3 *** 1.2048Age: Age: < 35 0.4207 241.8 *** 0.4663 < 35 -0.1329 28.5 *** -0.1489 > 54 -0.4678 71.8 *** -0.3822 > 54 0.0083 0.0 0.0040Married -0.6153 264.2 *** -0.6515 Married 0.2057 39.6 *** 0.2294Single Female 0.1317 9.4 *** 0.1057 Single Female -0.8770 357.4 *** -0.8109No. of Children 0.1700 221.2 *** 0.4858 No. of Children -0.1851 243.1 *** -0.4838No. of Deps Outside -0.2587 48.5 *** -0.3085 No. of Deps Outside 0.1716 67.7 *** 0.1756Health Ex. Or Good -0.0937 3.8 * -0.0553 Health Ex. Or Good -0.0292 0.3 -0.0171Own Home -0.9546 971.4 *** -0.8089 Own Home 0.2959 88.9 *** 0.3318Gift/Inheritance -1.0417 169.3 *** -0.6978 Gift/Inheritance 0.9165 516.9 *** 0.4415Staying Worker (WW) 0.3522 46.6 *** 0.3846 Staying Worker (WW) -1.2204 832.2 *** -0.9188Switching Worker (SW) 0.1807 5.8 ** 0.1028 Switching Worker (SW) -0.1680 9.3 *** -0.0879Switching Entrepeneur (SE) 0.9130 151.9 *** 0.4649 Switching Entrepeneur (SE) -0.7624 114.0 *** -0.2882Black X WW 1.3843 8.2 *** 0.8009 Black X WW -0.3294 2.2 -0.2526Black X SW 2.2004 16.3 *** 0.1790 Black X SW -0.2019 0.4 -0.0232Black X SE 1.6275 8.3 *** 0.1189 Black X SE -0.2301 1.1 -0.0293Middle Tier Wealth at Start 1.7967 1,773.3 *** 2.2249 Middle Tier Wealth at Start 1.2554 1,453.6 *** 1.4051

Minus 2 Log Liklihood 11,963 *** Minus 2 Log Liklihood 10,602 ***N 3,977 5,082

Level of statistical signficance: * = .10 ** = .05 ** = .01.# Time and region dummies are also included as control variables in regressions.Source: PSID Core and Supplemental Wealth Files and author's calculations.

34

Table 5: Regression Models Predicting Wealth-Income Ratios Combined Data for Entrepreneurs and Workers in 1984, 1989 and 1994

(1) All Families (2) Black (3) White (4) Entrprnrs (5) Workers

OLS Regressions Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-valIntercept 0.89247 3.5 *** 2.40573 5.6 *** 0.74704 2.5 ** 0.72833 0.5 1.04326 5.1 ***Black -0.31067 -2.1 ** -2.78077 -2.2 ** -0.03821 -0.3Age: < 35 -0.57085 -5.2 *** -0.70126 -3.4 *** -0.57184 -4.5 *** -1.17447 -1.8 * -0.47688 -5.5 *** > 54 1.28334 9.0 *** -0.30042 -1.0 1.37221 8.3 *** 1.61462 2.5 ** 0.98026 8.3 ***Education: HS Only 0.49264 3.5 *** 0.34166 1.4 0.52590 3.1 *** 0.83364 1.1 0.37641 3.3 *** Coll, no degree 0.52167 3.4 *** 0.52763 1.9 * 0.57655 3.1 *** 1.09203 1.4 0.29185 2.3 ** Coll degree 0.78557 5.1 *** -0.07812 -0.2 0.87115 4.8 *** 1.33695 1.8 * 0.46772 3.7 ***Married 0.07422 0.5 0.13088 0.5 0.07731 0.5 0.68974 0.9 -0.09058 -0.8Single Female -0.03035 -0.2 -0.08002 -0.3 -0.03066 -0.2 0.66396 0.7 -0.03313 -0.3No. of Children -0.06436 -1.4 -0.04518 -0.6 -0.06474 -1.2 0.14148 0.6 -0.08372 -2.2 **No. of Deps Outside -0.41956 -3.5 *** 0.05312 0.3 -0.53356 -3.6 *** 0.26753 0.5 -0.60886 -6.1 ***Worker -2.11683 -16.0 *** 0.44343 1.1 -2.24470 -15.1 ***Own Home 1.78559 15.5 *** 1.54303 7.2 *** 1.74980 12.9 *** 3.69009 5.7 *** 1.42065 15.5 ***Health Ex. Or Good 0.15535 0.9 0.04556 0.2 0.21265 1.0 0.32230 0.4 0.17786 1.3Income/1,000 -0.03910 -27.7 *** -0.08630 -12.4 *** -0.03810 -24.1 *** -0.06406 -13.6 *** -0.02960 -20.6 ***(Income/1,000)2 6.07E-06 5.0 *** 1.81E-04 4.7 *** 5.97E-06 4.4 *** 1.62E-05 5.1 *** 1.04E-05 6.5 ***Wealth 6.35E-06 98.7 *** 2.09E-05 55.0 *** 6.16E-06 86.5 *** 6.02E-06 24.9 *** 6.78E-06 117.3 ***Adjusted R2 0.46 0.43 0.49 0.31 0.54

Median Regressions Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-valIntercept 0.44856 13.8 *** -0.00544 -0.4 0.43352 8.7 *** 1.48126 5.2 *** 0.46327 14.0 ***Black -0.18104 -10.1 *** -0.95902 -4.7 *** -0.14682 -8.4 ***Age: Young -0.18314 -14.0 *** -0.00856 -1.6 -0.19763 -9.9 *** -0.30839 -2.6 *** -0.14665 -11.1 *** Old 0.43039 23.1 *** 0.09666 12.8 *** 0.46147 16.4 *** 0.80914 5.9 *** 0.24727 12.6 ***Education: HS Only 0.09806 5.7 *** 0.02424 4.3 *** 0.09253 3.4 *** 0.23054 1.5 0.07478 4.3 *** Coll, no degree 0.11894 6.3 *** 0.05856 8.4 *** 0.11824 4.0 *** 0.25847 1.6 0.09571 4.9 *** Coll degree 0.13191 6.9 *** 0.05074 6.1 *** 0.13659 4.7 *** 0.31994 2.0 ** 0.08995 4.5 ***Married 0.05239 2.9 *** -0.01295 -1.8 * 0.06731 2.4 ** 0.19476 1.2 0.03704 2.0 **Single Female -0.00957 -0.5 -0.02692 -4.0 *** 0.01557 0.5 -0.33517 -1.6 -0.01065 -0.5No. of Children -0.04568 -8.5 *** -0.00765 -4.1 *** -0.04588 -5.5 *** -0.05170 -1.1 -0.03267 -6.0 ***No. of Deps Outside -0.01401 -1.0 -0.00087 -0.1 -0.04548 -1.9 * 0.02388 0.2 -0.00435 -0.3Worker -0.42896 -26.4 *** 0.04565 5.2 *** -0.45348 -19.1 ***Own Home 0.66059 47.0 *** 0.17740 33.1 *** 0.68782 31.9 *** 1.73182 13.2 *** 0.53394 37.6 ***Health Ex. Or Good 0.07803 3.7 *** 0.00265 0.4 0.08804 2.6 *** -0.54725 -3.1 *** 0.09126 4.2 ***Income/1,000 -0.01214 -72.9 *** 0.00517 32.4 *** -0.01313 -54.3 *** -0.03133 -34.4 *** -0.01383 -62.2 ***(Income/1,000)2 -1.56E-05 -148.1 *** -1.77E-04 -302.6 *** -1.48E-05 -97.5 *** -3.85E-06 -8.7 *** -1.01E-06 -4.0 ***Wealth 8.24E-06 975.8 *** 1.93E-05 989 *** 8.13E-06 679.9 *** 7.42E-06 137.3 *** 9.51E-06 819 ***Pseudo R2 0.43 0.38 0.44 0.37 0.46 N 14,495 4,732 9,763 1,796 12,699* = .10 ** = .05 and *** = .01. Time and region dummies are also included as control variables in regressions.Source: PSID core and supplemental data files and the author's calculations.

35

Table 6: Descriptive Statistics for Black and White Entrepreneurs and Workers Combined Statistics for 1984-89 and 1989-94

Black White Families Families N 1,874 4,543 Mean Saving Rate for Group 5.43% 8.79% Mean Saving Rate by Category: Staying Entrepreneurs 18.29% 10.29% Switching Entrepreneurs 5.20% 11.24% Switching Workers 31.17% 16.55% Staying Workers 4.69% 7.83% Education: Less than H.S. 21.13% 13.54% H.S. only 44.77% 32.41% Coll, no degree 20.47% 22.70% Coll degree 12.80% 30.99% Age: Young (21 - 34) 45.71% 39.65% Middle (35 - 54) 48.46% 50.81% Old (55 and over) 5.83% 9.54% Male Head of Household 55.86% 85.40% Married Couple 38.14% 67.50% Mean No. of Children 1.0433 0.9427 Mean No. of Deps Outside 0.1501 0.0827 Health Ex. Or Good 84.71% 94.15% Own Home 41.48% 67.66% Receive Gift or Inher. 1.21% 7.20% Mean Real Wealth Start ($) 43,185 207,116 Mean Real Wealth End ($) 54,681 250,156 Mean Real Total Income ($) 209,764 369,581 Mean Change in Real Wealth ($) 11,496 43,040 Staying Entrepreneurs 52 526 Switching Entrepreneurs 39 178 Switching Workers 51 278 Staying Workers 1,732 3,561 Source: PSID core and supplemental wealth files and author's calculations.

36 Table 7: Regression Models Predicting Saving Rates Combined Data for Entrepreneurs and Workers in 1984-89 and 1989-94

(4) Staying (5) Staying (1) Combined (2) Black (3) White Entrepreneurs Workers

OLS Regressions Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-valIntercept -0.214189 -1.6 0.104432 1.0 -0.227046 -1.3 -0.521027 -1.3 -0.116371 -0.8Black -0.022730 -0.3 0.123876 0.2 -0.070558 -0.9Age : < 35 0.032978 0.6 -0.088259 -2.0 ** 0.044567 0.7 0.598730 3.4 *** -0.068840 -1.2 > 54 -0.104506 -1.2 0.103371 1.2 -0.096806 -0.9 -0.048144 -0.3 0.108439 1.1Educ: H. S. Only 0.036295 0.5 0.016882 0.3 0.031416 0.3 -0.347963 -1.6 0.056983 0.7 Coll, no degree -0.037239 -0.5 0.049165 0.8 -0.053801 -0.5 -0.274794 -1.2 0.020827 0.2 Coll degree -0.120827 -1.5 0.159997 2.2 ** -0.149317 -1.5 -0.539363 -2.5 ** 0.007320 0.1Married -0.031693 -0.4 -0.003563 -0.1 -0.027798 -0.3 0.284148 1.3 -0.053781 -0.7Single Female 0.208489 2.5 ** -0.076308 -1.3 0.227247 2.2 ** 0.167941 0.5 0.128929 1.5No. of Children -0.035676 -1.6 -0.038382 -2.3 ** -0.033079 -1.2 -0.024242 -0.4 -0.035433 -1.4No. of Deps Outside 0.058882 1.1 0.021789 0.9 0.073933 1.0 0.072805 0.4 -0.010681 -0.2Health Ex. Or Good -0.001172 0.0 0.015077 0.3 0.015685 0.1 0.170896 0.6 0.012384 0.1Own Home 0.028274 0.5 0.191675 4.2 *** 0.033640 0.5 0.123348 0.6 0.138670 2.2 **Gift/Inheritance 2.07E-06 3.1 *** 1.39E-05 4.4 *** 2.05E-06 2.6 *** 1.60E-06 2.1 ** 3.84E-06 2.6 ***Staying Worker -0.486458 -6.4 *** -0.533557 -3.8 *** -0.469611 -5.2 ***Switching Worker -0.395817 -2.4 ** -0.473828 -2.5 ** -0.271275 -1.9 *Switching Entrepeneur -0.469220 -2.1 ** 0.059729 0.4 -0.380512 -2.3 **Perm. Income/1,000 0.001540 9.5 *** -0.000397 -0.8 0.001552 8.1 *** 0.001926 6.4 *** 0.001107 4.6 ***(Perm. Income/1,000)2 -9.33E-09 -0.2 1.39E-06 1.9 * -2.29E-08 -0.5 -2.19E-07 -3.4 *** 2.85E-07 3.1 ***Starting Real Wealth -1.27E-06 -37.7 *** -4.16E-06 -65.5 *** -1.22E-06 -30.8 *** -5.96E-07 -12.6 *** -2.48E-06 -46.2 ***Adjusted R2 0.18 0.49 0.17 0.26 0.29

Median Regressions Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-val Coeff. t-valIntercept -0.047408 -3.3 *** -0.009565 -0.7 -0.063332 -2.9 *** -0.034667 -0.2 -0.033569 -3.2 ***Black -0.004228 -0.5 -0.027750 -0.2 -0.014934 -3.4 ***Age : Young 0.004538 0.9 -0.009806 -1.4 -0.005458 -0.7 0.103728 1.4 -0.011750 -3.0 *** Old 0.026106 2.7 *** 0.018731 1.2 0.028842 2.0 ** -0.053356 -0.6 0.034788 4.4 ***Educ: H. S. Only 0.010802 1.5 0.011234 2.1 ** 0.014218 1.3 0.013411 0.1 0.009974 2.0 * Coll, no degree 0.016257 2.0 ** 0.016550 1.5 0.021144 1.8 * -0.098925 -1.0 0.011137 1.9 * Coll degree 0.030703 3.7 *** 0.001461 0.1 0.028998 2.4 ** -0.096041 -1.0 0.022571 3.6 ***Married 0.001621 0.2 -0.014735 -1.6 0.008837 0.8 0.119813 1.2 -0.009076 -1.6Single Female 0.000594 0.1 -0.011516 -1.5 0.005574 0.4 0.291300 1.6 -0.002939 -0.5No. of Children -0.015349 -6.9 *** 0.000850 0.4 -0.014932 -4.5 *** -0.058792 -2.1 ** -0.008185 -5.0 ***No. of Deps Outside 0.010775 1.6 0.017408 1.2 -0.003602 -0.4 -0.072012 -1.0 0.003675 0.9Health Ex. Or Good 0.008688 0.9 0.010489 1.8 * 0.012441 0.8 -0.030247 -0.3 0.014563 2.2 **Own Home 0.010417 1.8 * 0.079776 2.2 ** 0.009294 1.1 -0.032579 -0.4 0.023800 5.7 ***Gift/Inheritance 2.10E-06 33.2 *** 8.72E-06 1.5 2.01E-06 22.7 *** 1.50E-06 14.0 *** 4.33E-06 42.7 ***Staying Worker -0.137286 -17.4 *** -0.219468 -2.0 ** -0.137084 -4.8 ***Switching Worker -0.051769 -4.3 *** -0.010687 -0.1 -0.043530 -1.5Switching Entrepeneur -0.165312 -11.7 *** -0.178537 -2.4 ** -0.160995 -5.4 ***Perm. Income/1,000 0.000302 18.0 *** -0.000093 -0.7 0.000333 13.8 *** 0.000824 6.3 *** 0.000299 16.4 ***(Perm. Income/1,000)2 5.51E-08 12.4 *** 1.25E-06 2.7 *** 7.82E-08 12.0 *** -6.40E-08 -2.8 *** 1.37E-07 16.5 ***Starting Real Wealth -6.13E-07 -174.3 *** -3.72E-06 -3.1 *** -7.47E-07 -138.4 *** -6.01E-07 -24.6 *** -1.18E-06 -330.1 ***Pseudo R2 0.07 0.20 0.07 0.09 0.08

N 6,464 1,885 4,579 586 5,328

37

Source: PSID Data and the author's calculations.

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